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Light emerging from natural water bodies and measured by radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities for characterizing aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission; the NASA Surface Biology and Geology designated observable mission; and NASA Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP-AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries, and oceans. Specific in situ measurements include remote-sensing reflectance, irradiance reflectance, and coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering. The dataset can be downloaded from https://doi.org/10.1594/PANGAEA.902230 (Casey et al., 2019).

We present a neural network methodology for clustering large data sets into pertinent groups. We applied this methodology to analyze the phytoplankton absorption spectra data gathered by the Laboratoire d'Oceanographie de Villefranche. We first partitioned the data into 100 classes by means of a self-organizing map (SOM) and then we clustered these classes into 6 significant groups. We focused our analysis on three POMME campaigns. We were able to interpret the absorption spectra of the samples taken in the first oceanic optical layer during these campaigns, in terms of seasonal variability. We showed that spectra from the PROSOPE Mediterranean campaign, which was conducted in a different region, were strongly similar to those of the POMME-3 campaign. This analysis led us to propose regional empirical relationships, linking phytoplankton absorption spectra to pigment concentrations, that perform better than the previously derived overall relation. OCIS codes: 010.4450, 010.7340.

Spectral absorption coefficients of phytoplankton can now be derived, under some assumptions, from hyperspectral ocean color measurements and thus become accessible from space. In this study, multilayer perceptrons have been developed to retrieve information on the pigment composition and size structure of phytoplankton from these absorption spectra. The retrieved variables are the main pigment groups (chlorophylls a, b, c, and photosynthetic and nonphotosynthetic carotenoids) and the relative contributions of three algal size classes (pico-, nano-, and microphytoplankton) to total chlorophyll a. The networks have been trained, tested, and validated using more than 3700 simultaneous absorption and pigment measurements collected in the world ocean. Among pigment groups, chlorophyll a is the most accurately retrieved (average relative errors of 17% and 16% for the test and validation data subsets, respectively), while the poorest performances are found for chlorophyll b (average relative errors of 51% and 40%). Relative contributions of algal size classes to total chlorophyll a are retrieved with average relative errors of 19% to 33% for the test subset and of 18% to 47% for the validation subset. The performances obtained for the validation data, showing no strong degradation with respect to test data, suggest that these neural networks might be operated with similar performances for a large variety of marine areas. OCIS codes: 010.0010, 010.4450, 010.7340.

We present a statistical analysis of a large set of absorption spectra of phytoplankton, measured in natural samples collected from ocean water, in conjunction with detailed pigment concentrations. We processed the absorption spectra with a sophisticated neural network method suitable for classifying complex phenomena, the so-called self-organizing maps (SOM) proposed by Kohonen [Kohonen, Self Organizing Maps (Springer-Verlag, 1984)]. The aim was to compress the information embedded in the data set into a reduced number of classes characterizing the data set, which facilitates the analysis. By processing the absorption spectra, we were able to retrieve well-known relationships among pigment concentrations and to display them on maps to facilitate their interpretation. We then showed that the SOM enabled us to extract pertinent information about pigment concentrations normalized to chlorophyll a. We were able to propose new relationships between the fucoxanthin/Tchl-a ratio and the derivative of the absorption spectrum at 510 nm and between the Tchl-b/Tchl-a ratio and the derivative at 640 nm. Finally, we demonstrate the possibility of inverting the absorption spectrum to retrieve the pigment concentrations with better accuracy than a regression analysis using the Tchl-a concentration derived from the absorption at 440 nm. We also discuss the data coding used to build the self-organizing map. This methodology is very general and can be used to analyze a large class of complex data.

The Green Edge project was designed to investigate the onset, life and fate of a phytoplankton spring bloom (PSB) in the Arctic Ocean. The lengthening of the ice-free period and the warming of seawater, amongst other factors, have induced major changes in arctic ocean biology over the last decades. Because the PSB is at the base of the Arctic Ocean food chain, it is crucial to understand how changes in the arctic environment will affect it. Green Edge was a large multidisciplinary collaborative project bringing researchers and technicians from 28 different institutions in seven countries, together aiming at understanding these changes and their impacts into the future. The fieldwork for the Green Edge project took place over two years (2015 and 2016) and was carried out from both an ice-camp and a research vessel in the Baffin Bay, canadian arctic. This paper describes the sampling strategy and the data set obtained from the research cruise, which took place aboard the Canadian Coast Guard Ship (CCGS) Amundsen in spring 2016. The dataset is available at https://doi.org/10.17882/59892 (Massicotte et al., 2019a). [ABSTRACT FROM AUTHOR]

The maximum quantum yield of carbon fixation (phi(sub C max)) ranges from 0.005 to 0.063 mol C/mol quanta in the natural phytoplankton colony of the oligotrophic sites in tropical northeast Atlantic Ocean. Photosynthetic parameters are nearly constant with depth at the eutrophic and mesotrophic sites. A deep chlorophyll maximum exists at the oligotrophic site. Nitrate concentration is a likely contributing factor for the two-fold variation in phi(sub C max), while the three-fold variations are explained by the light absorption contribution of non-photosynthetic pigments.

The spectral chlorophyll-specific absorption coefficients of phytoplanktonic populations at mesotrophic and oligotrophic sites in the tropical North Atlantic show vertical and site-specific variations. The oligotrophic site has the largest vertical variability, while it is lower for the mesotrophic site. The presence of non-photosynthetic carotenoids explains the observed variation in the fluorescence yield. A huge depression in the blue part of the spectrum suggests spectral selectivity. The chromatic adaptation by phytoplanktons to spectral irradiance is discussed.

This paper introduces the new Polarization and Directionality of the Earth's Reflectances (POLDER) instrument. The spaceborne POLDER sensor, which is selected to fly aboard the Japanese ADEOS satellite scheduled for launch in early 1996, has nearly completed phase C of its development at the Centre National d'Etudes Spatiales, the French space agency. To prepare for the 1996 space mission, airborne prototypes are being tested and evaluated in the framework of various measurement campaigns. The POLDER sensor is designed to collect global observations of polarized and directional solar radiation reflected by the Earth-atmosphere system for climate and global change studies. Aboard the ADEOS platform, the POLDER mission will provide near-daily coverage of the Earth at 6 X 7 [km.sup.2] resolution. The POLDER system will offer unprecedented opportunities to observe biophysical parameters over the oceans and land surfaces. The sensor's unique features, when compared to current and planned spaceborne instruments, include its ability to: 1) measure polarized reflectance in the visible and near-infrared spectral regions; 2) observe Earth target reflectance from 12 directions during a single satellite pass; and 3) operate in two dynamic modes in order to achieve both high signal to noise ratio and wide dynamic range. Six of POLDER's eight channels are optimized for observing atmospheric aerosols, clouds, ocean color, and land surfaces. The other two are centered on the [H.sub.2]O and [O.sub.2] absorption bands for retrieving atmospheric water vapor amount and cloud top altitude, respectively. POLDER data will be subject to the high calibration standards defined by the POLDER mission team, with absolute calibration accuracies of 2% for the shorter wavelength channels ([Lambda] [is less than or equal to] 565 nm) and 3% for the longer wavelengths. A 1% accuracy is the goal for the intercalibration between the spectral channels. The POLDER instrument aboard ADEOS will contribute significantly to climate-related research on aerosol cycling, cloud-radiation interactions, the Earth radiation budget, ocean primary productivity, and continental biosphere dynamics. Using POLDER capability to measure bidirectional reflectance and polarization distribution functions for estimating biophysical parameters, the POLDER mission's scientific objectives will be to: 1) map atmospheric aerosols, including their sources and transport, and study their influence on the Earth radiation budget; 2) assess cloud properties, namely their height, phase and type; 3) estimate total integrated water vapor amount; 4) improve Earth radiation budget estimates; 5) estimate chlorophyll-like pigment content in the ocean surface layer and its role in the carbon cycle; and 6) characterize land surface properties and vegetation cover. In meeting these objectives, POLDER will play an important role in advancing the measurement and research objectives of the World Climate Research Program (WCRP) and the International Geosphere and Biosphere Program (IGBP).

The the light backscattering, absorption, attenuation properties and the size distribution function of nine phytoplankters were determined. Backscattering data is important in determiningthe contribution of phytoplankton to the reflectance of upper oceanic waters. The study protocol involved the combination of an integrating sphere with a spectroradiometerLI-1800 UW. The results indicated that living algae have a negligible effect on the ocean backscattering process. On the other hand, it was suggested that other particles associated with algae such as bacteria and detritus are mainly responsible for backscattering.

This study assesses marine biological production of organic carbon based on the diel variability of bio-optical properties monitored by two BioGeoChemical-Argo (BGC-Argo) floats. Experiments were conducted in two distinct Mediterranean systems, the Northwestern Ligurian Sea and the Central Ionian Sea during summer months. We derived particulate organic carbon (POC) stock and gross community production integrated within the surface, euphotic and subsurface chlorophyll maximum (SCM) layers, using an existing approach applied to diel cycle measurements of the particulate beam attenuation (cp) and backscattering (bbp) coefficients. The diel cycle of cp provided a robust proxy for quantifying biological production in both systems; that of bbp was comparatively less robust. Derived primary production estimates vary by a factor of 2 depending upon the choice of the bio- optical relationship that converts the measured optical coefficient to POC, which is thus a critical step to constrain. Our results indicate a substantial, yet variable, contribution to the water column production of the SCM layer (16-42%). In the Ligurian Sea, the SCM is a seasonal feature that behaves as a subsurface biomass maximum (SBM) with the ability to respond to episodic abiotic forcing by increasing production. In contrast, in the Ionian Sea, the SCM is permanent, induced by phytoplankton photoacclimation and contributes moderately to water column production. These results emphasize the strong potential for transmissometers deployed on BGC-Argo profiling floats to quantify non- intrusively in situ biological production of organic carbon in the water column of stratified oligotrophic systems with recurring or permanent SCMs, which are widespread features in the global ocean. [ABSTRACT FROM AUTHOR]

The MALINA oceanographic campaign was conducted during summer 2009 to investigate the carbon stocks and the processes controlling the carbon fluxes in the Mackenzie River estuary and the Beaufort Sea. During the campaign, an extensive suite of physical, chemical and biological variables was measured across seven shelf–basin transects (south-north) to capture the meridional gradient between the estuary and the open ocean. Key variables such as temperature, absolute salinity, radiance, irradiance, nutrient concentrations, chlorophyll-a concentration, bacteria, phytoplankton and zooplankton abundance and taxonomy, and carbon stocks and fluxes were routinely measured onboard the Canadian research icebreaker CCGS Amundsen and from a barge in shallow coastal areas or for sampling within broken ice fields. Here, we present the results of a joint effort to tidy and standardize the collected data sets that will facilitate their reuse in further studies of the changing Arctic Ocean. The dataset is available at https://doi.org/10.17882/75345 (Massicotte2020b). [ABSTRACT FROM AUTHOR]